Biosensor method and system based on feature vector extraction

ABSTRACT

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

The United States Government has rights in this invention pursuant tocontract no. DEAC05-00OR22725 between the United States Department ofEnergy and UT-Battelle, LLC.

FIELD OF THE INVENTION

The present invention relates to biosensors for detecting chemical,biological and/or radiological contaminants in water or air, and moreparticularly to biosensor systems and related methods for sensing oftoxins based on analysis of time-dependent changes in signals providedby the biosensors responsive to the toxin.

BACKGROUND OF THE INVENTION

There is an increased awareness of the possibility of attacks onmetropolitan areas using chemical, biological and radiological warfareagents. Researchers at Oak Ridge National Laboratory have developed abiosensor system to detect toxic agents in primary-source drinkingwater, such as disclosed in U.S. Pat. No. 6,569,384 to Greenbaum et al.through the analysis of fluorescence induction curves. FIGS. 1(b, d, f,and h) illustrate exposure fluorescence induction curves recorded every5 minutes when exposed to different toxic agents and data collection foreach curve is within 10 seconds, while FIGS. 1(a, c, e, and g) providecontrols (no toxins).

In order to detect the existence of toxic agents, the traditional methodis to measure the so-called “efficiency of PSII (photosystem II)photochemistry”,${{PSII}\quad{efficiency}} = \frac{F_{\max} - F_{s}}{F_{\max}}$where F_(s) is the value at the stable time and F_(max) is the maximumvalue of the fluorescence induction curve, as shown in FIG. 2. The PSIIefficiency represents a simple induction curve-based calculation of thefluorescence signal “signature”, and significant deviations thereofindicate the potential presence of a toxic agent in the water.

Although PSII efficiency is generally effective in detecting thepresence of toxic agents, it fails in some cases due to thenon-significant photochemical yield presented by certain toxic agents.Moreover, it cannot classify between different agents or the same agentwith different concentrations. In addition, using this parameter it cantake as long as 60 minutes to arrive at a decision regarding detectionof a contamination event. The classification of different agents with ashorter response time is of profound importance, such as to reduceresponse time to a contamination event. With the knowledge of the typeof toxic agent, appropriate medicine and rescue strategies can be usedin time to save lives as well as counter the terrorist attacks.

FIG. 3(a) compares the average PSII efficiencies of controlled inductioncurves and the curves obtained from water exposed to four differenttoxic agents, and FIG. 3(b) shows the average standard deviation of eachtoxic-agent-exposed signal to its corresponding control signal. It isobserved that there is no deterministic pattern in the deviation of PSIIefficiency between control and exposure signals to indicate the presenceof a specific toxic agent.

SUMMARY

A method of biosensor-based detection of toxins comprises the steps ofproviding at least one time-dependent control signal generated by abiosensor in a gas or liquid medium (e.g. water), and obtaining atime-dependent biosensor signal from the biosensor in the gas or liquidmedium to be monitored or analyzed for the presence of one or moretoxins selected from chemical, biological or radiological agents. Thetime-dependent biosensor signal is processed to obtain a plurality offeature vectors using at least one of amplitude statistics and atime-frequency analysis. At least one parameter relating to toxicity ofthe gas or liquid medium is then determined from the feature vectorsbased on reference to the control signal.

The time-frequency analysis can comprises wavelet coefficient analysis.In a preferred embodiment of the invention both amplitude statistics andtime-frequency analysis are used in the processing step.

The biosensors can comprise naturally-occurring, free-living, indigenousphotosynthetic organisms when the liquid medium is water. In thisembodiment, the time-dependent biosensor signal can comprisefluorescence induction data.

The method can also include the step of identifying which toxin(s) arepresent in the gas or liquid medium. In a preferred embodiment, a lineardiscriminant method is used for the identifying step, such as supportvector machine (SVM) classification.

A water or air quality sensor system comprises a biosensor in an air orwater medium to be monitored or analyzed for the presence of one or moretoxins selected from chemical, biological or radiological agents, adetector proximate to the biosensor for measuring a time-dependentbiosensor signal from the biosensor, and a processor for analyzing thetime-dependent biosensor signal to obtain a plurality of feature vectorsusing at least one of amplitude statistics and time-frequency analysis.The processor then determines at least one parameter relating totoxicity of the air or water medium from the feature vectors. The systemcan further comprise a memory for storing at least one time-dependentcontrol signal, wherein the processor analyzes the time-dependentbiosensor signal to obtain the parameter from the feature vectors basedon reference to the control signal. The system preferably includes aclassifier for identifying which toxins are present in the air or watermedium.

BRIEF DESCRIPTION OF THE DRAWINGS

A fuller understanding of the present invention and the features andbenefits thereof will be obtained upon review of the following detaileddescription together with the accompanying drawings, in which:

FIGS. 1(b, d, f, and h) illustrate conventional exposure fluorescenceinduction curves recorded every 5 minutes when exposed to differenttoxic agents and data collection for each curve is within 10 seconds,while FIGS. 1(a, c, e, and g) provide controls.

FIG. 2 shows conventional PS II efficiency parameters based on afluorescence induction curve, F_(s) being the value at a stable time andF_(max) being the maximum value of the fluorescence induction curve.

FIG. 3(a) compares conventional average PSII efficiencies of controlledinduction curves and the curves obtained from water exposed to fourdifferent toxic agents, while FIG. 3(b) shows the average standarddeviation of each toxic-agent-exposed signal to its correspondingcontrol signal.

FIG. 4(a)-(d) provides some examples to illustrate the effect ofdifferent curves on the amplitude statistics.

FIG. 5 compares the average amplitude statistics generated from thecontrol and exposure signals of each toxic agent class.

FIG. 6 shows a 3-D plot showing three amplitude statistics features(mean, standard deviation, and skewness). It can be seen from FIG. 6that data from different classes of toxic agents cluster at differentpositions within the 3D feature space that can be separated relativelyeasily.

FIG. 7 shows some examples of some exemplary mother wavelets, includingthe Daubechies wavelet, the Coiflet wavelet, the Harr wavelet, theSymmlet wavelet, the Meyer wavelet and the Battle Lemarie wavelet.

FIG. 8 shows a filter bank implementation of a discrete wavelettransform.

FIG. 9 shows an example of a fluorescence induction curve exposed to 10mM KCN and the corresponding 3-level wavelet decomposition.

FIG. 10(a)-(d) show fluorescence induction curves exposed to fourclasses of toxic agents and the corresponding wavelet transforms ofthese signals.

FIG. 11 is a schematic of an exemplary biosensor system for carrying outthe method of present invention.

FIG. 12 shows the extracted features of 5 mM potassium cyanide (KCN) inthe Clinch River samples as functions of time. The toxic agent is addedinto the sample at time 0.

FIG. 13 shows the extracted features of 5 mM KCN in the Chlamydomonassamples as function of time. The toxic agent is added into the samplesat time 0.

FIG. 14 shows the extracted features of 10 mM KCN in the Clinch Riversamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 15 shows the extracted features of 10 mM KCN in the Chlamydomonassamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 16 shows the extracted features of 20 μM Diuron (DCMU) in theClinch River samples as functions of time. The toxic agent is added intothe samples at time 0.

FIG. 17 shows the extracted features of 20 μM DCMU in the Chlamydomonassamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 18 shows the extracted features of 225 μM Paraquat in the ClinchRiver samples as functions of time. The toxic agent is added into thesamples at time 0.

FIG. 19 shows the extracted features of 225 μM Paraquat in theChlamydonomas samples as functions of time. The toxic agent is addedinto the samples at time 0.

FIG. 20 shows the extracted features of 40 μM MPt in the Clinch Riversamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 21 shows the extracted features of 40 μM MPt in the Chlamydonomassamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 22 shows the extracted features of 60 μM MPt in the Clinch Riversamples as functions of time. The toxic agent is added into the samplesat time 0.

FIG. 23 shows the extracted features of 60 μM MPt in the Chlamydonomassamples as functions of time. The toxic agent is added into the samplesat time 0.

DETAILED DESCRIPTION

A method of biosensor-based detection of toxins comprises the steps ofproviding at least one time-dependent control signal generated by abiosensor in a liquid (e.g. water) or a gas (e.g. air), and obtaining atime-dependent biosensor signal from the biosensor in a gas or liquidmedium to be monitored or analyzed for the presence of one or moretoxins selected from chemical, biological or radiological agents. Thetime-dependent biosensor signal is processed to obtain a plurality offeature vectors using at least one of amplitude statistics and atime-frequency analysis. At least one parameter relating to the toxicityof the gas or liquid medium is then determined from the feature vectorsbased on reference to the control signal provided. As used herein, thephrase “feature vector” is defined as (i) summation based statisticalmeasures as described below (amplitude statistics) and (ii) coefficients(e.g. wavelet coefficients), or statistical parameters derived from thecoefficients (e.g. wavelet coefficient standard deviation) generated byapplication of a time-frequency analysis to the time-dependent sensorsignal.

As noted in the background, conventional aquatic biosensors monitor thequality of primary-source drinking water by analyzing the fluorescencesignal signature from healthy algae during photosynthesis. Fluorescenceemitted by healthy algae differs from that emitted by algae exposed to atoxic agent. Simple algorithms based on PSII efficiency are generallyused to characterize the signature of the fluorescence signal withoutany time-frequency analysis or high-order statistical analysis.

In contrast, applied to fluorescence data, the invention describesalgorithms which better characterize features of the fluorescence signalsuch that more detailed information can be obtained, such as detectionof a toxic agent with a higher confidence level, the identification ofdifferent types of toxic agents, the detection of toxic agents atdifferent concentration levels, as well as a more robust response thatis less affected by the photo-inhibition effect or diurnal cycle on thealgae. The invention also permits substantially more rapid assessment topermit arriving at a decision regarding detection of a contaminationevent in generally no more than several minutes (CHECK), as compared toup to 60 minutes using conventional fluorescence signature analysis.

A first new algorithm comprises high-order statistical analysis(referred to herein as “amplitude statistics”) of the light signal inthe time domain. As used herein, the phrase “amplitude statistics” isdefined as summation based statistical measures derived from a pluralityof (N) time points in the signal curve, such as first order (mean),second order (standard deviation), third order (skewness), and fourthorder (kurtosis). PS II efficiency as described in U.S. Pat. No.6,569,384 to Greenbaum et al. is thus clearly not amplitude statisticssince the measurement therein is based on the simple difference betweendiscrete points being the maximum value of the fluorescence inductioncurve (F_(max)) and the fluorescence value at the stable time (F_(s)).

Amplitude statistics can capture more dynamic features of the signalthan PSII efficiency, including how fast the signal approaches maximumand minimum, how far samples are from the mean value, and how symmetricthe signal appears. These features are generally required in thedetection and identification regarding the existence of different toxicagents.

A first new algorithm comprises wavelet analysis of the light signal inthe time-frequency domain referred to herein as “time-frequencyanalysis”. Because of the nature of the light signal captured by theaquatic biosensors, time-frequency analysis can reveal when and how thefrequency of the signal changes. In a preferred embodiment, only threefeatures extracted from the wavelet coefficients are used in thealgorithm instead of the entire set of coefficients for signalcharacterization.

Amplitude statistics and time-frequency analysis according to theinvention can be used independently to provide detection resultssignificantly improved as compared to algorithms based on thefluorescence signal signature. However, by combining amplitudestatistics and time-frequency analysis, the confidence detection andidentification can be improved to an even higher level.

The gas or liquid medium to be monitored or analyzed is generally air inthe case of gas and water in the case of liquid. The water can beprimary-source drinking water. In a preferred embodiment of theinvention, algae is the biosensor used to generate time-dependentbiosensor signals such as fluorescence induction curves which areanalyzed through extraction of feature vectors to permit classificationof different toxic agents in sunlight-exposed primary-source drinkingwater based on feature vectors. As described in the Examples below,agents studied included methyl parathion (MPt), potassium cyanide (KCN),Diuron (DCMU), and Paraquat in both the samples of Clinch River(Tennessee) and the samples with lab-grown Chlamydomonas reinhardtii.The Examples provided demonstrate superior performance of the claimedmethodology through three groups of experimental results, including thecapabilities of toxic agent detection, multi-type toxic agentclassification, and immunity to the effect of photo-inhibition ordiurnal cycle.

Biosensors are generally cell-based, and can include geneticallymodified cells. For example, a bacterium modified with lux genes can beused. In the case of fluorescence induction, algae can be used, eithernaturally-occurring or genetically modified. Naturally-occurring aquaticalgae does not generally require culturing.

Every water source that is exposed to sunlight contains populations ofphotosynthetic microorganisms (phytoplankton and algae, for example), atconcentrations ranging from 10 to as high as 100,000 organisms/ml.Although always present in sunlight-exposed water, these microorganismsare often invisible to the unaided eye. Phytoplankton emits acharacteristic fluorescence signal that, if detectable in solutions withlow microorganism concentrations, can be utilized as an in situindicator of chemical and/or biological warfare agents water supplies.Biosensors provide time-dependent biosensor signal while in a gas orliquid medium to be monitored or analyzed for the presence of one ormore toxins selected from chemical, biological or radiological agents.Water-soluble toxic chemical and/or biological agents, for example, caninclude blood agents (cyanide, for example), pesticides (methylparathion, for example) and herbicides (DCMU, for example), orradionuclide that could pose a threat to primary-source drinking watersupplies.

The time-dependent biosensor signal is modified by the toxin as comparedto a control signal when the toxin is absent. A variety of signal typescan be analyzed using the invention. For example, the signals can bespectroscopic (e.g. fluorescent). Regarding spectroscopic signals, see,e.g., Huang, G. G., Yang, J. 2005 “Development of infrared opticalsensor for selective detection of tyrosine in biological fluids”,Biosensors and Bioelectronics, 21(3):408-418. Regarding acousticsignals, see, e.g., U.S. Pat. No. 6,486,588 to Doron, et al. “Acousticbiosensor for monitoring physiological conditions in a body implantationsite”; “Acoustic immunosensor for real-time sensing of neurotransmitterGABA”, Proceedings of the 25^(th) IEEE Annual International Conference,4:2998-3000. +Khraiche, M. L., Zhou, A., Muthuswamy, J. 2003, and“Acoustic sensors for monitoring neuronal adhesion in real-time”,Proceedings of the 25^(th) IEEE Annual International Conference,3:2186-2188.). Regarding electrochemical signals, see, e.g., U.S. Pat.No. 6,511,854 to Asanov, et al. “Regenerable biosensor using totalinternal reflection fluorescence with electrochemical control”, and“Development and evaluation of electrochemical glucose enzyme biosensorsbased on carbon film electrodes” Talanta, 65(2):306-312. +Xu, J.-Z., etal. 2004.

Regarding thermal detection, see e.g.,“Calorimetric biosensors withintegrated microfluidic channels. Biosensors and Bioelectronics”,19(12):1733-1743. +Towe, B. C., Guilbeau, E. J. 1996. Regarding magneticbased sensors, see de Oliveira, J. F., et al. 2005 “Magnetic resonanceas a technique to magnetic biosensors characterization in Neocapritermesopacus termites” Journal of Magnetism and Magnetic Materials,292(2):e171-e174. +Chemla, Y. R., et al. 2000, “Ultrasensitive magneticbiosensor for homogeneous immunoassay”, Proc. Natl. Acad. Sci. USA,97(26):14268-72. Regarding surface plasmon resonance (SPR) using enzymesor antibodies see, e.g., U.S. Pat. No. 6,726,881 to Shinoki, et al.“Measurement chip for surface resonance biosensor”, U.S. Pat. No.6,573,107 to Bowen, et al. “Immunochemical detection of an explosivesubstance in the gas phase through surface plasmon resonancespectroscopy”, U.S. Pat. No. 5,313,264 to Ivarsson, et al. “Opticalbiosensor system”.

In the case of air monitoring using algae-based biosensors, the algaegenerally requires culturing. In this embodiment, air to be analyzed canbe drawn through filter paper having algae cultured thereon.

Although the invention is generally hereafter described related tofluorescence induction provided by algal biosensors in water, as notedabove, the invention is in no way limited to this specific embodiment.

Feature Extraction of Fluorescence Induction Curves

Classification of different toxic agents in primary-source drinkingwater through the analysis of fluorescence induction curves is achallenging undertaking. It is difficult to separate different curves bysimply looking at the amplitude responses of the curves. Statisticalanalysis according to the invention can describe, for example, how“fast” the curve reaches the maximum, how “slow” the curve decreasesafter reaching the maximum. These features are largely related tohigh-order statistics. In addition, further analyses in othertransformation domains (frequency or time-frequency) as described beloware also preferably performed in order to provide additional informationrelated to the frequency change over time.

Amplitude Statistics

Amplitude statistics provide statistical measurements of the biosensorsignal to be analyzed, such as average fluorescence amplitude over time.The mathematical definition of amplitude statistics up to the fourthorder is as follows:${{mean}\text{:}\mu_{amp}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{F(i)}}}$${{standard}\quad{deviation}\text{:}\sigma_{amp}} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {{F(i)} - \mu_{amp}} \right)^{2}}}$${{skewness}\text{:}\gamma_{amp}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( \frac{{F(i)} - \mu_{amp}}{\sigma_{amp}} \right)^{3}}}$${{kurtosis}\text{:}\beta_{amp}} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( \frac{{F(i)} - \mu_{amp}}{\sigma_{amp}} \right)^{4}}}$where, regarding fluorescence, F(i) represents the relative fluorescenceat the ith time point, and N is the number of time points in theinduction curve.

FIG. 4 provides some examples to illustrate the effect of differentcurves on the amplitude statistics. The first-order statistics, themean, is actually the average of amplitude, as shown in FIG. 4(a). Thesecond-order statistics, the standard deviation, reflects how differenteach sample is from the mean, as shown in FIG. 4(b). The third-orderstatistics, the skewness, depicts how symmetric the curve is. In FIG.4(c), the skewness is compared for two different curves. It is obviousthat the left curve is less symmetric than the right curve andcorrespondingly, the skewness of the left curve is much larger (1.369)than that of the right curve (0.369). The fourth-order statistics, thekurtosis, describes how flat the curve is. The flatter the curve, theless the kurtosis, which can be observed by the two example curves shownin FIG. 4(d).

FIG. 5 compares the average amplitude statistics generated from thecontrol and exposure signals of each toxic agent class. FIG. 6 shows the3-D plot regarding to the three amplitude statistics features (mean,standard deviation, and skewness). It can be seen from FIG. 6 that datafrom different classes of toxic agents cluster at different positionswithin the 3D feature space that can be separated relatively easily.This data demonstrates the effectiveness of using amplitude statisticsin feature extraction to classify among different toxic chemical agents.

Statistics in Wavelet Coefficients

The wavelet transform is a generalization of the well-known Fouriertransform in signal processing. The Fourier transform represents asignal in the frequency domain by decomposing a waveform into sinusoidsof different frequencies with different amplitudes (weights) which sumto the original waveform. In another word, the Fourier transform revealsthe amount of each frequency component needed to form the originalwaveform. Although informative, the Fourier transform does not preserveany information concerning the time domain, e.g., when and how long inthe time domain that a specific frequency component occurs. The lack oftime-domain information in the Fourier transform presents a criticalproblem for the analysis of non-stationary signals which do not maintainthe same frequency component throughout the duration of the signal. Forexample, a stationary signal with four frequency components (e.g. 10 Hz,25 Hz, 50 Hz, and 100 Hz) at all times, and a non-stationary signal withthe same four frequency components occurring at different time periodswill have the same Fourier transform despite the obvious differencepresented in the respective time-domain signals.

Unlike the Fourier transform, time-frequency analysis, such as providedby the wavelet transform, presents a time-frequency representation ofthe signal. A time-frequency representation of the signal providestime-domain information that a specific spectral component occurs. Sincebiosensor signals such as the fluorescence induction signals aregenerally non-stationary (See FIG. 1), it is beneficial to apply atime-frequency analysis such as the wavelet transform to present boththe time-domain and the frequency-domain information.

Different from the Fourier transform that uses the sine and cosinefunctions as basis functions, wavelet transforms use basic functionsthat are localized in both time and frequency domains. Wavelettransforms aim at representing the time function in terms of simple,fixed models, which are called wavelets. Wavelets are derived from asingle generating function that is called the mother wavelet. The motherwavelet meets the following conditions:${{\int{{\psi(t)}{\mathbb{d}t}}} = 0},{{\psi_{a,b}(t)} = {\frac{1}{\sqrt{a}}{\psi\left( \frac{t - b}{a} \right)}}}$where a is the scaling factor and b is the translation factor. Thetranslation and scaling of the mother wavelet will generate a family offunctions. The parameter a changes the scale of the wavelet, that is,the greater |a| is, the smaller the frequency. The parameter b controlsthe translation of the wavelet. In other words, wavelet transforms usenarrower windows when the signal frequencies are high and wider windowswhen the signal frequencies are low. This representation allows thewavelet transform to “enlarge” every high-frequency component, such asthe transient in signals. This is one of the main advantages of thewavelet transform over the Fourier transform. The wavelet transformcould be categorized into the continuous wavelet transform (CWT) and thediscrete wavelet transform (DWT), defined as:${{CWT}_{f}\left( {a,b} \right)} = {{a}^{{- 1}/2}{\int_{- \infty}^{\infty}{{f(t)}{\psi\left( \frac{t - b}{a} \right)}\quad{\mathbb{d}t}}}}$DWT_(f)(m, n) = a₀^(−m/2)∫_(−∞)^(∞)f(t)ψ(a₀^(−m)t − nb₀)  𝕕tConsidering the computation complexity of CWT, given sampled signals offinite length, it is generally preferable to use the DWT with theinvention. In the DWT, the family of wavelets is given as:ψ_(m,n)(t)=a ₀ ^(−m/2)ψ(a ₀ ^(−m) t−nb ₀)

Some examples of some exemplary mother wavelets are shown in FIG. 7,including the Daubechies wavelet, the Coiflet wavelet, the Harr wavelet,the Symmlet wavelet, the Meyer wavelet and the Battle Lemarie wavelet.Among these wavelets, the Daubechies wavelet is popularly used in theengineering field. The parameters of the corresponding Daubechieswavelets are given by sequence (h₀, h₁, . . . , h_(N)) that satisfiesthe following conditions:${{\sum\limits_{n = 0}^{N}h_{n}} = \sqrt{2}},{{\sum\limits_{n = 0}^{N}{\left( {- 1} \right)^{n}n^{k}h_{n}}} = 0},{{{for}\quad k} = 0},1,2,\cdots\quad,{\left( {N - 1} \right)/2},{and}$${{\sum\limits_{n = 0}^{N - {2k}}{h_{n}h_{n + {2k}}}} = 0},{{{for}\quad k} = 1},2,3,\cdots\quad,{\left( {N - 1} \right)/2.}$

In practice, the forward and inverse wavelet transforms could beimplemented using a set of sampling functions, called digital filterbanks as shown in FIG. 8. The sampled data f[n] is first input inparallel to a low-pass filter (H[n]) and a high-pass filter (G[n]). Theoutputs from the two filters are down-sampled and thus kept exactly onehalf of the size of the input signals. After the first stage, the outputof the high-pass filter becomes the first-level wavelet coefficients,C1. The second stage uses the output from the low-pass filter of theprevious step as the input, which is again sent to H[n] and G[n]. Theoutput of the down-sampled high-pass filter of this stage becomes thesecond-level wavelet coefficients, C2. The same process continues untilonly two sample points are left. In another word, the DWT uses filtersof different cutoff frequencies to analyze the signal at differentscales. The signal is passed through a series of high-pass and low-passfilters to analyze the high frequency and low frequency contentsrespectively. FIG. 8 shows a three-level wavelet transform with fourseries of coefficients, C1 to C4. FIG. 9 shows an example of afluorescence induction curve exposed to 10 mM KCN and the corresponding3-level wavelet decomposition.

FIG. 10(a)-(d) show fluorescence induction curves exposed to fourclasses of toxic agents and the corresponding wavelet transforms ofthese signals. In a preferred embodiment, each original induction curveis preprocessed by down-sampling them into signals with 256 discretesamples and then apply the DWT. Suppose the highest frequency componentexisted in the fluorescence induction signal is f_(max), then in athree-level wavelet decomposition, the C1 wavelet coefficients of 128samples correspond to spectral components within frequency band(f_(max)/2, f_(max)), the C2 coefficients of 64 samples reflectfrequency components of band (f_(max)/4, f_(max)/2), the C3 coefficientsof 32 samples correspond to band (f_(max)/8, f_(max)/4), and the C4coefficients of 32 samples are the low-pass content of band (0,f_(max)/4). FIG. 9 shows one example of the 3-level decomposition.

Since the fluorescence induction signals are mainly composed ofvery-low-frequency components, only the first 64 wavelet coefficients(C4 and C3) that correspond to low-frequency spectral components areconsidered and shown in FIG. 10.

Existing methods use the wavelet coefficients themselves to serve as afeature set for classification. However, not all the coefficients arenecessary. In addition, the use of all generated coefficients wouldincrease the dimensionality of the feature set and bring bothcomputation burden and the “insufficient training sample” problem.Therefore, it is generally preferable to choose to calculate only threestatistical features from the first 64 wavelet coefficients: the mean,the variance, and the energy. The mean of the first 64 waveletcoefficients is plotted in FIG. 10 as an example to show the distinctionbetween control and exposure data of different toxic agent classes.

The use of the wavelet transform in pattern recognition has been a hotresearch area. Many new algorithms are used to develop robust featurevectors from the wavelet transform. These algorithms normally requiremore complex computation. Since algorithms according to the inventionaims at real-time signal processing, these simplified feature extractioncould save-computation cost and facilitate real-time response.

Classifier Design

A supervised classifier is preferably used to differentiate amongdifferent toxic agents. Among all existing supervised classificationalgorithms, a linear discriminant method is preferred, such as thesupport vector machine (SVM) technique (Duda, R. O., Hart, P. E., Stork,D. G. 2001, Pattern classification; John Wiley & Sons, 2nd edition). TheSVM classifier relies on transforming the data to represent patterns ina much higher dimension than the original feature space. With anappropriate nonlinear mapping (which is application specific) to asufficiently high dimension, data from different categories can beseparated by hyperplanes. The optimal hyperplane between each pair ofclasses is decided by the support vectors that are the most informativetraining samples for the classification task. Compared to otherclassification methods, SVM solves the problem of overfitting since thecomplexity of the classifier is characterized by the number of supportvectors instead of the dimensionality of the transformed space. Beforeapplying the classifier, a normalization phase is preferably conductedbased on the features extracted from the raw data using algorithmsdescribed above.

An automated biosensor system 10 for carrying out the method of presentinvention is shown schematically in FIG. 11. A fluorometer 12 isattached to a cell 14 so that a cell window 16 faces the fluorometerinput 18. The cell has an inlet 20 having an optional particulate filter36 and an outlet 26 for passing water therethrough. A pump 24 drawswater from the outlet 26 and expels same through an exit 28. The cell 14could have a displacement pump which draws water into the cell andexpels same through a common inlet/outlet opening (analogous to 20),obviating outlet 26 and exit 28. Any means for introducing water intothe cell and discharging water from the cell is suitable for carryingout the present invention.

The fluorometer 12 must be of sufficient sensitivity for measuringphotosynthetic-activity of naturally-occurring, free-living, indigenousphotosynthetic organisms drawn into the cell 14 with sample water.Applicants have used a Walz XE-PAM pulse-amplitude-modulationfluorometer available from Heinz Walz GmbH•Eichenring 6•D-91090Effeltrich•GERMANY Phone: +49-(0)9133/7765-0•Telefax:+49-(0)9133/5395•E-Mail: info@mail.walz.com. The Walz XE-PAM fluorometeris described in detail at the following Internet web site:http://www.walz.com/pamzta.htm.

The fluorometer is electrically connected by a connector 32 to anelectronics package 30, which includes a power supply, systems foroperating the fluorometer 12 and pump 24, data processing electronics,and a transmitter that transmits a signal through an antenna 34. Theelectronics package 30 contains commonly used devices that are wellknown in the art. The particular components that are used therein, andthe particular method of gathering and transmitting data are notcritical to the operation of the present invention. The processorpreferably implementing both amplitude statistics and a time-frequencyanalysis can be co-located with electronics package 30, or at a remotesite having antenna 24.

Operation of the biosensor 10 can be constant sampling or intermittentsampling. Intermittent operation can be random sampling or timedsampling. The pump 24 is operated to cause water to flow through thecell 14. The fluorometer 12 is activated to measure fluorescence in thewater flowing through the cell 14. In a preferred embodiment, theelectronics package 30 implements a processor running both amplitudestatistics and a time-frequency analysis for analyzing raw data from thefluorometer 12, and emits a signal through the antenna 34 indicating thepresence and/or absence of chemical warfare agent(s) in the water, aswell as the identity of the agent(s). The signal is received byequipment that indicates and/or records the data.

EXAMPLES

It should be understood that the Examples described below are providedfor illustrative purposes only and do not in any way define the scope ofthe invention.

Performance Evaluation based on Fluorescence Induction Using AmplitudeStatistics and Time-Frequency Analysis

Through three designed experiments, the superior efficiency of theinventive methodology was demonstrated for the detection of toxicagents, the classification of multi-type toxic agents, as well as theimmunity to photo-inhibition. Fluorescence induction data set of normalprimary-source drinking water samples as well as samples exposed to fourdifferent toxic agents at different concentrations were collected. Thecontrol and toxic-agent-exposed fluorescence induction data were takenevery 5 minutes after the dark adaptation for 15 minutes.

-   -   20 μM DCMU,    -   5 mM/10 mM KCN,    -   40 μM/60 μM MPt, and    -   225 μM Paraquat.

Experiment 1—Toxic Agent Detection

The objective of the first experiment performed was to evaluate theperformance of the inventive methodology in detecting the presence oftoxic agents. FIGS. 12-23 show the extracted features (including PSII,amplitude statistics, and wavelet coefficient statistical features) ofeach specific toxic agent as functions of time. The toxic agents wereadded into the samples at time 0, therefore, the signals with negativetime index correspond to the control data and the signals during thepositive time period are the agent-exposed data collected from eitherthe Clinch River samples or the lab-grown Chlamydomonas reinhardtiisamples. At each time moment, three samples were collected and featurevalues calculated. The mean and standard deviation of these three valuesare used to illustrate the performance curve at that time moment. Toexamine the change of feature values over time, the signal collected attime 0 as reference was chosen and yield at each time moment calculated(feature at time t—feature at time 0)/feature at time 0. The followingnotations are used to describe these performance curves:

-   -   μ_(c)(t), σ_(c)(t): the mean and the standard deviation of the        feature values derived from the three control samples collected        at moment t (with a negative time index)    -   μ_(e)(t), σ_(e)(t): the mean and the standard deviation of the        feature values derived from the three exposure samples collected        at moment t (with a positive time index)        ${\mu_{c} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{\mu_{c}(t)}}}},{\sigma_{c} = {{\frac{1}{T - 1}\sqrt{\sum\limits_{t = 1}^{T}\left( {{\mu_{c}(t)} - \mu_{c}} \right)^{2}}}:}}$        the mean and the standard deviation of the mean feature value        for the control signal        ${\mu_{c} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{\mu_{c}(t)}}}},{\sigma_{c} = {\frac{1}{T - 1}\sqrt{\sum\limits_{t = 1}^{T}\left( {{\mu_{e}(t)} - \mu_{e}} \right)^{2}}}}$        : the mean and the standard deviation of the mean feature value        for the exposure signal        $p_{c} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sigma_{c}(t)}}}$        : the average standard deviation for the control signals. This        metric refers to the degree of error for the measurements in the        three experiments.        $p_{c} = {\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sigma_{c}(t)}}}$        : the average standard deviation for the exposure signals.

In order to quantitatively evaluate the effectiveness of each feature indifferentiating between the control and the exposure signals ofdifferent toxic agents, two metrics are introduced: the Fisher criterionand the average confidence level. The Fisher criterion originates frompattern classification (Duda, et al., 2001) where a linear projection ispursued in order to best separate two classes. In this work, thedefinition of Fisher criterion is used to quantify different performancecurves in differentiating between the control and the exposure signals.In general, the Fisher criterion looks for the feature that maximizesthe difference between the mean value of feature derived from thecontrol and the exposed signal while minimizing the variance among them,as formulated below:$M_{1} = \frac{{{\mu_{c} - \mu_{e}}}^{2}}{\sigma_{c}^{2} + \sigma_{e}^{2}}$

Table 1 lists the value of the Fisher criterion to evaluate the effectof using different features for agent detection. In both Table 1 andTable 2 “Clinch” refers to Clinch River samples and “Chlamy” tolab-grown Chlamydomonas reinhardtii samples. It can be seen from Table 1below that the first-order amplitude statistics (mean) gives the bestmetric evaluation for most of the toxic agents, while the waveletanalysis, the standard deviation and the skewness in amplitudestatistics also contribute to the differentiation between control andexposure signals in some cases. TABLE 1 Metric to evaluate thedifference between the control and the exposure signals of a specifictoxic agent. Clinch Chlamy Clinch Chlamy Clinch Chlamy Clinch ChlamyClinch Chlamy Clinch Chlamy 5 mM 5 mM 10 mM 10 mM 20 μM 20 μM 225 μM 225μM 40 μM 40 μM 60 μM 60 μM KCN KCN KCN KCN DCMU DCMU Paraquat ParaquatMPt MPt MPt MPt PSII 17.7130 5.0371 22.7318 5.5293 62.8073 63.9 40.355033.4259 7.1502 7.8844 16.4704 11.7760 Amplitude 35.5863 36.0766 45.524667.4501 356.4241 2111.9 0.6992 9.2416 5.1574 15.3435 9.9401 20.3104Statistics Mean Amplitude 28.6817 0.9657 36.6538 1.2782 62.5213 2.314.8945 55.4867 28.2660 0.0137 10.6912 0.0643 Statistics StandardDeviation Amplitude 0.8372 95.4071 0.5331 161.6205 64.7360 835.9 21.89970.1661 3.2165 2.0009 0.6709 4.4742 Statistics Skewness Amplitude 6.354655.1439 5.5483 40.3244 71.3140 242.9 13.0980 0.5380 9.3028 0.7696 2.70101.8269 Statistics Kurtosis Wavelet 2.0168 6.4232 5.1258 10.1957 37.33505.3 88.1866 7.4516 75.2339 15.7935 148.3582 22.2547 Coefficient MeanWavelet 2.1605 5.4072 7.6813 9.5367 88.0769 7.8 103.1261 13.2129102.9558 19.1866 196.7953 23.6347 Coefficient Standard Deviation Wavelet2.4914 6.9769 8.8526 13.0129 93.8024 8.2 121.8164 15.7150 159.363629.3503 258.6794 29.5307 Coefficient Energy

The second metric is to measure the average standard deviationscorresponding to the control and the exposure signals respectively.Since three experiments are conducted at each time index, a standarddeviation can be calculated which in turn shows the degree of error ofthe measurement. Then the average standard deviation can be calculatedover time. Table 2 shows the average standard deviation for each toxicagent experiment. The features with the smallest standard deviation forcontrol and exposure signals are highlighted respectively, whichcorrespond to the lowest degree of error in the measurements. TABLE 2Average confidence levels corresponding to the control and the exposuresignals for each toxic agent experiments. Chlamy Clinch Chlamy ClinchChlamy Clinch Chlamy Clinch 225 μM Clinch Chlamy Clinch Chlamy 5 mM 5 mM10 mM 10 mM 20 μM 20 μM 225 μM Para- 40 μM 40 μM 60 μM 60 μM KCN KCN KCNKCN DCMU DCMU Paraquat quat MPt MPt MPt MPt PSII Control 7.0917 0.52111.6890 0.5517 1.0858 2.0746 1.7486 1.4760 1.0621 0.4878 2.2364 0.3403Expo- 6.5320 1.7093 18.3632 1.7922 2.0 6.7009 4.7046 2.7538 2.14790.4307 1.8623 1.6207 sure Amplitude Control 5.2388 0.5618 0.8539 0.88121.5700 0.3313 1.4978 1.0451 0.7550 0.4799 1.0815 0.4611 Statistics Expo-0.9510 0.9920 7.9207 0.7628 5.8 3.8638 2.1024 3.2456 1.1627 1.02153.2440 0.8095 Mean sure Amplitude Control 2.5006 3.5376 4.5622 9.63183.1649 4.2325 2.3274 2.1803 1.1564 3.3941 2.8899 2.0820 Statistics Expo-3.7121 2.2964 21.9756 8.8715 4.2 11.0207 4.5965 3.0236 2.5287 2.40952.8635 3.6592 Standard sure Deviation Amplitude Control 88.5797 3.396738.0090 6.9013 83.9859 5.5045 28.9698 2.8750 46.2854 3.0154 20.61122.4801 Statistics Expo- 589.4564 8.5795 46.7603 5.5052 1828.5 119.9891115.5963 3.8739 178.6413 3.3092 32.4242 3.2171 Skewness sure AmplitudeControl 14.1784 7.9964 3.9196 18.4167 5.9547 7.9097 4.6823 4.1893 3.08495.9877 3.1575 4.5517 Statistics Expo- 30.0245 14.6607 37.0559 19.0062186.0 104.7981 15.0406 4.8179 13.2075 5.8021 5.0936 6.4004 Kurtosis sureWavelet Control 4.4131 2.0694 4.6845 4.8448 3.9575 2.0216 2.3834 1.71932.3628 2.2244 2.3272 0.9600 Coefficient Expo- 10.0056 2.8299 8.89912.8766 3.5 6.6531 3.1095 2.2675 3.8647 1.7896 2.8639 1.7446 Mean sureWavelet Control 4.0981 0.9213 2.3761 0.7798 3.4715 0.7323 2.0943 1.50471.8607 2.0927 1.9234 0.5228 Coefficient Expo- 11.1806 1.7690 10.55361.4078 4.0 6.0873 2.7939 1.9375 3.8073 2.1660 2.6258 1.5849 Standardsure Deviation Wavelet Control 4.1592 0.9518 2.5547 1.0033 3.5281 0.78462.1229 1.5015 1.9128 2.0593 1.9717 0.5363 Coefficient Expo- 11.12801.8272 10.4410 1.2675 3.8 6.0681 2.8405 1.9898 3.8230 2.1425 2.62071.5795 Energy sure

Experiment 2—Classification of Different Toxic Agents

After the detection of the presence of a toxic agent, a preferred nexttask is to classify among different types of toxic agents to identifythe toxic agent(s) present. The second experiment was aimed atevaluating the performance of the classifier in differentiating amongdifferent toxic agents. Corresponding to the toxic agent exposuresignals used in the data set acquired, this experiment deals with afour-class classification problem, which is to differentiate among KCN,MPt, DIMP, and Paraquat exposure signals.

A five-fold cross-validation was conducted to evaluate the performanceof the designed system. The data set was randomly divided into fivesubsets of equal size, each of which is tested using the classifiertrained on the remaining four subsets. The cross-validation accuracy isthe average percentage of data that are correctly classified, which onthe other hand, shows the confidence of classification.

Table 3 provides the classification accuracies when using differentcombinations of features within different response time. In theterminology of classification, the classification accuracy is referredto as the probability of a sample to be correctly classified, whichequals 1 minus the probability of error. It is observed from Table 3that instead of a generally increasing classification accuracy overtime, the conventional PSII efficiency feature actually performs worsewhen the response time increases. The combination of amplitudestatistics and wavelet coefficient according to a preferred embodimentof the invention still continuously perform better than other features.However, in order to obtain an accuracy of above 90%, it was found to benecessary to wait till the response time passes 10 minutes. This is thetrade-off between providing higher classification capabilities and beingable to respond in a shorter period of time. TABLE 3 Classificationaccuracy obtained from experiment 2 (4-class classification: KCN, MPt,Disopropyl Methylphosphonate (DIMP), and Paraquat) 5 min 10 min 15 minPSII Amplitude Wavelet Classification Classification Classificationefficiency statistics coefficient accuracy (%) accuracy (%) accuracy (%)X 54.84 51.61 51.09 X 70.97 88.71 85.87 X 67.74 70.97 72.83 X X 77.4287.10 85.87 X X 61.29 79.03 86.96 X X 77.42 91.94 89.13 X X X 80.6591.94 86.96

Experiment 3—The Effect of Photo-Inhibition

The last experiment was performed to examine the effect ofphoto-inhibition in the classification of fluorescence inductive datasets exposed to different toxic agents. Photo-inhibition is a biologicalphenomenon of algae when the temperature of the primary-source drinkingwater samples increases. The characteristics of fluorescence inductioncurves during photo-inhibition are very similar to the curves of toxicagent exposed signals. Therefore, it is essential in real-worldapplications to eliminate the effect of photo-inhibition since thedrinking water source can be exposed to a toxic agent during noon orearly afternoon hours when photo-inhibition occurs.

To demonstrate the advantage of the inventive methodology indifferentiating between photo-inhibition and toxic-agent-exposure, theclassification on a data set including both the control signals withphoto-inhibition and the signals exposed to different toxic agents wasapplied. Table 4 below lists the classification accuracies for threedata sets that are composed of normal control signals, photo-inhibitioncontrol signals, and toxic agent exposure signals

The results of all the three data sets show that the inclusion ofphoto-inhibition does not measurably affect the performance ofclassification either between control vs. exposure, or between theexposures of different toxic agents. TABLE 4 Classification accuracywith photo-inhibition. Classification Data set accuracy (%) 4-class:Control (normal and Photo- 93.94 inhibition), KCN (5 mM and 10 mM), 20μM MPt, 30 μM Paraquat using water samples from the Clinch River2-class: Control (normal and Photo- 100.0 inhibition), 2 mM KCN usingsamples tested with lab-grown Chlamydomonas 2-class: Control (normal andPhoto- 97.37 ihhibition), 300 μM Paraquat using samples tested withlab-grown Chiamydomonas

It is to be understood that while the invention has been described inconjunction with the preferred specific embodiments thereof, that theforegoing description as well as the examples which follow are intendedto illustrate and not limit the scope of the invention. Other aspects,advantages and modifications within the scope of the invention will beapparent to those skilled in the art to which the invention pertains.

1. A method of biosensor-based detection of toxins, comprising the stepsof: providing at least one time-dependent control signal generated by abiosensor in a gas or liquid medium; obtaining a time-dependentbiosensor signal from said biosensor in said gas or liquid medium to bemonitored or analyzed for the presence of one or more toxins selectedfrom chemical, biological or radiological agents; processing saidtime-dependent biosensor signal to obtain a plurality of feature vectorsusing at least one of amplitude statistics and a time-frequencyanalysis, and determining at least one parameter relating to toxicity ofsaid gas or liquid medium from said feature vectors based on referenceto said control signal.
 2. The method of claim 1, wherein saidtime-frequency analysis comprises wavelet coefficient analysis.
 3. Themethod of claim 1, wherein both said amplitude statistics andtime-frequency analysis are used in said processing step.
 4. The methodof claim 1, wherein said liquid medium is water.
 5. The method of claim4, wherein said biosensors comprise naturally-occurring, free-living,indigenous photosynthetic organisms in said water.
 6. The method ofclaim 5, wherein said time-dependent biosensor signal comprisesfluorescence induction data.
 7. The method of claim 1, furthercomprising the step of identifying which of said toxins are present insaid gas or liquid medium.
 8. The method of claim 7, wherein a lineardiscriminant method is used for said identifying step.
 9. The method ofclaim 8, wherein said linear discriminant method comprises supportvector machine (SVM) classification.
 10. A water or air quality sensorsystem, comprising: a biosensor in an air or water medium to bemonitored or analyzed for the presence of one or more toxins selectedfrom chemical, biological or radiological agents; a detector proximateto said biosensor for measuring a time-dependent biosensor signal fromsaid biosensor, and a processor for analyzing said time-dependentbiosensor signal to obtain a plurality of feature vectors using at leastone of amplitude statistics and time-frequency analysis, said processordetermining at least one parameter relating to toxicity of said air orwater medium from said feature vectors.
 11. The system of claim 10,further comprising a memory for storing at least one time-dependentcontrol signal, wherein said processor analyzes said time-dependentbiosensor signal to obtain said parameter from said feature vectorsbased on reference to said control signal.
 12. The system of claim 10,wherein said system includes a classifier for identifying which of saidtoxins are present in said air or water medium.